Contents 
import pandas as pd 
import plotly.express as px 

data = pd.read_csv('../data/life-expectancy.csv')
data.head()
Entity Code Year Life expectancy
0 Afghanistan AFG 1950 27.638
1 Afghanistan AFG 1951 27.878
2 Afghanistan AFG 1952 28.361
3 Afghanistan AFG 1953 28.852
4 Afghanistan AFG 1954 29.350 
data = data.sort_values(by='Life expectancy', ascending=False)
data.head()
Entity Code Year Life expectancy
11005 Monaco MCO 2019 86.751
11004 Monaco MCO 2018 86.560
11003 Monaco MCO 2017 86.325
11002 Monaco MCO 2016 86.049
11001 Monaco MCO 2015 85.739 
fig = px.line(data, x='Year', y='Life expectancy', color='Entity')
fig.show() 

asia = data[data['Entity'] == 'Asia']

asia
Entity Code Year Life expectancy
1010 Asia NaN 2019 73.593
1009 Asia NaN 2018 73.381
1008 Asia NaN 2017 73.150
1007 Asia NaN 2016 72.897
1006 Asia NaN 2015 72.620
... ... ... ... ...
941 Asia NaN 1950 41.115
940 Asia NaN 1913 28.100
939 Asia NaN 1900 28.000
938 Asia NaN 1880 27.500
937 Asia NaN 1770 27.500

74 rows × 4 columns

import plotly.express as px
fig = px.choropleth(asia, locations="Entity",
                    color="Life expectancy", # lifeExp is a column of gapminder
                    hover_name="Entity",
                     animation_frame='Year', # column to add to hover information
                    color_continuous_scale=px.colors.sequential.Plasma)

fig.update_layout(
        autosize=False,
        margin = dict(
                l=0,
                r=0,
                b=0,
                t=0,
                pad=4,
                autoexpand=True
            ),
            width=800,
        #     height=400,
    )

for k in range(len(fig.frames)):
    fig.frames[k]['layout'].update(title_text=f'My title {k}')

fig.show()

data.columns

Index(['Entity', 'Code', 'Year', 'Life expectancy'], dtype='object')

fig = px.bar(data, y='Life expectancy', x='Year', text='Life expectancy')
fig.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig.update_layout(uniformtext_minsize=8, uniformtext_mode='hide')
fig.show()

data.Year.unique() 

array([2019, 2018, 2017, 2016, 2015, 2014, 2013, 2012, 2011, 2010, 2009,
       2008, 2007, 2006, 2005, 2004, 2003, 2002, 2001, 2000, 1999, 1998,
       1997, 1996, 1995, 1994, 1993, 1992, 1991, 1990, 1989, 1988, 1987,
       1986, 1985, 1984, 1983, 1982, 1981, 1980, 1979, 1978, 1977, 1976,
       1975, 1974, 1973, 1972, 1971, 1970, 1969, 1968, 1967, 1966, 1965,
       1964, 1963, 1962, 1961, 1960, 1959, 1958, 1957, 1956, 1955, 1954,
       1953, 1952, 1951, 1950, 1949, 1948, 1947, 1946, 1942, 1943, 1945,
       1944, 1939, 1938, 1941, 1937, 1936, 1940, 1934, 1935, 1933, 1932,
       1931, 1930, 1928, 1926, 1925, 1929, 1923, 1924, 1922, 1927, 1921,
       1917, 1913, 1920, 1919, 1914, 1909, 1915, 1916, 1910, 1911, 1912,
       1905, 1907, 1906, 1902, 1908, 1918, 1904, 1903, 1898, 1901, 1895,
       1897, 1896, 1900, 1879, 1892, 1893, 1889, 1888, 1894, 1880, 1878,
       1886, 1887, 1854, 1899, 1858, 1891, 1885, 1870, 1884, 1881, 1890,
       1855, 1865, 1856, 1857, 1872, 1860, 1859, 1866, 1877, 1851, 1871,
       1873, 1883, 1850, 1869, 1864, 1853, 1882, 1852, 1849, 1846, 1867,
       1874, 1875, 1861, 1862, 1863, 1868, 1835, 1876, 1845, 1825, 1823,
       1841, 1836, 1848, 1824, 1847, 1844, 1840, 1843, 1838, 1826, 1842,
       1822, 1583, 1780, 1827, 1833, 1839, 1816, 1578, 1837, 1776, 1828,
       1798, 1813, 1573, 1805, 1830, 1818, 1832, 1808, 1774, 1815, 1794,
       1797, 1618, 1817, 1787, 1820, 1803, 1802, 1821, 1792, 1753, 1648,
       1633, 1831, 1628, 1568, 1760, 1804, 1608, 1793, 1553, 1829, 1775,
       1799, 1653, 1773, 1788, 1796, 1548, 1807, 1814, 1603, 1834, 1708,
       1703, 1791, 1751, 1767, 1759, 1758, 1777, 1698, 1593, 1766, 1786,
       1598, 1781, 1761, 1778, 1754, 1673, 1819, 1755, 1588, 1795, 1713,
       1801, 1613, 1563, 1782, 1770, 1748, 1693, 1733, 1643, 1764, 1768,
       1756, 1806, 1688, 1765, 1783, 1718, 1723, 1771, 1763, 1752, 1738,
       1812, 1769, 1811, 1743, 1638, 1543, 1785, 1757, 1790, 1623, 1800,
       1663, 1658, 1779, 1668, 1762, 1678, 1810, 1784, 1683, 1789, 1772,
       1809, 1728, 1558])

import plotly.express as px
fig = px.choropleth(data, locations="Code",
                    color="Life expectancy", # lifeExp is a column of gapminder
                    hover_name="Entity",
                     animation_frame='Year', # column to add to hover information
                    color_continuous_scale=px.colors.sequential.Plasma)

fig.update_layout(
        autosize=False,
        margin = dict(
                l=0,
                r=0,
                b=0,
                t=0,
                pad=4,
                autoexpand=True
            ),
            width=800,
        #     height=400,
    )

for k in range(len(fig.frames)):
    fig.frames[k]['layout'].update(title_text=f'My title {k}')

fig.show()

import plotly.express as px

df = px.data.gapminder().query("year==2007")
fig = px.choropleth(df, locations="iso_alpha",
                    color="lifeExp", # lifeExp is a column of gapminder
                    hover_name="country", # column to add to hover information
                    color_continuous_scale=px.colors.sequential.Plasma)
fig.show()

df.head()
country continent year lifeExp pop gdpPercap iso_alpha iso_num
11 Afghanistan Asia 2007 43.828 31889923 974.580338 AFG 4
23 Albania Europe 2007 76.423 3600523 5937.029526 ALB 8
35 Algeria Africa 2007 72.301 33333216 6223.367465 DZA 12
47 Angola Africa 2007 42.731 12420476 4797.231267 AGO 24
59 Argentina Americas 2007 75.320 40301927 12779.379640 ARG 32